In this paper, we investigate collaborative active learning, a paradigm in which multiple collaborators explore a new domain by leveraging their combined machine learning capabilities without disclosing their existing data and models. Instead, the collaborators share prediction results from the new domain and newly acquired labels. This collaboration offers several advantages: (a) it addresses privacy and security concerns by eliminating the need for direct model and data disclosure; (b) it enables the use of different data sources and insights without direct data exchange; and (c) it promotes cost-effectiveness and resource efficiency through shared labeling costs. To realize these benefits, we introduce a collaborative active learning framework designed to fulfill the aforementioned objectives. We validate the effectiveness of the proposed framework through simulations. The results demonstrate that collaboration leads to higher AUC scores compared to independent efforts, highlighting the framework's ability to overcome the limitations of individual models. These findings support the use of collaborative approaches in active learning, emphasizing their potential to enhance outcomes through collective expertise and shared resources. Our work provides a foundation for further research on collaborative active learning and its practical applications in various domains where data privacy, cost efficiency, and model performance are critical considerations.

翻译：本文研究了协作主动学习这种范式，在该范式中，多个协作者通过利用其组合的机器学习能力探索新领域，而无需披露其现有数据和模型。取而代之的是，协作者共享来自新领域的预测结果以及新获取的标签。这种协作提供了若干优势：(a) 通过消除直接披露模型和数据的需求，解决了隐私和安全问题；(b) 无需直接数据交换即可利用不同的数据源和见解；(c) 通过共享标签成本，促进了成本效益和资源效率。为了实现这些益处，我们引入了一个旨在满足上述目标的协作主动学习框架。我们通过模拟验证了所提框架的有效性。结果表明，与独立工作相比，协作带来了更高的AUC分数，突显了该框架克服单个模型局限性的能力。这些发现支持在主动学习中采用协作方法，强调了通过集体专业知识和共享资源提升结果的潜力。我们的工作为协作主动学习的进一步研究及其在数据隐私、成本效率和模型性能均为关键考虑因素的各种领域中的实际应用奠定了基础。